With the large amounts of data generated in recent years, data mining and machine learning are playing an increasingly important role in today's computing environment. For example, businesses may utilize either data mining or machine learning to predict the behavior of users. This predicted behavior may then be used by businesses to determine which plan to proceed with, or how to grow the business.
Several algorithms have been created in these fields. One such algorithm is Random Forests. Such algorithms use multiple random points of data in order to make predictions. There are two methods to sample random data. The first method is sample with replacement (SwR), and the second is sample without replacement (SwoR).
Typically, SwR is the preferred method to sample random data since a selection will not affect the probability of subsequent selections. However, as datasets grow in size, some containing trillions of records, it is becoming increasingly difficult to generate an SwR sample that is sufficiently large and random for machine learning or data analytics purposes.
There is a need, therefore, for an improved method, article of manufacture, and apparatus for managing data.